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Unslopping AI

Claim: we’ve solved the AI slop problem (!) 💩🧹✨

Blog post: https://facebookresearch.github.io/RAM/blogs/unslop/ by: Jason Weston.

Key idea: take *expert* human writing and learn rubrics that find the gap between experts and models. Train with those rubrics.

We train with RL-XAR (RL with eXpert Aligned Rubrics) & see large performance gains on writing scientific paper sections, Pulitzer prize novel continuations and high quality Wikipedia pages.

First: The failure of standard LLM Judgements 💀

On paper writing tasks, strong judges (GPT-5.6 or Opus-4.8) think current ‘slop’ models are better than humans on selected high quality papers (using either pairwise, or using standard rubrics).

Our method can learn rubrics where the human is considered better by the grader (right in fig) – the key to training.

Continue reading “Unslopping AI” | >

Autonomous Lipid Nanoparticle Engineering

Researchers at the Massachusetts Institute of Technology have developed an automated production system for lipid nanoparticles (LNPs) that offers unprecedented control over their size and shape, significantly accelerating the design of targeted RNA and DNA therapeutics. While LNPs serve as crucial delivery vehicles for mRNA vaccines and other nucleic acids, traditional manufacturing relies on time-consuming trial-and-error and cannot reliably control particle dimensions, which dictate their behavior and targeted organs in the body. Building on a previously developed two-step fluid mixing technique, the newly autonomous platform integrates real-time dynamic light scattering to continuously monitor particle formation and adjust parameters on the fly. By leveraging data gathered from this automated system, the team also trained a machine-learning model capable of predicting the exact manufacturing conditions required to engineer LNPs with specific sizes and non-spherical shapes. This streamlined, data-driven approach eliminates previous manufacturing guesswork, providing a powerful tool to rapidly engineer customized delivery vehicles for next-generation genetic therapies.

Lipid nanoparticles (LNPs) are the leading vehicles for encapsulating and delivering nucleic acid therapeutics. Yet, their process development remains labor-intensive and empirical, constrained by coupled quality attributes and limited mechanistic insight. We present an autonomous, pilot-scale platform for accelerating LNP process development by identifying critical process parameters (CPPs) that produce LNPs with target size attributes. The platform combines a size-control production method with inline dynamic light scattering (DLS) for real-time feedback, enabling closed-loop experimentation and accelerated optimization. With built-in automated design of experiments, dynamic parameter sweeps, and Bayesian optimization, the platform enables rapid, data-rich exploration of complex design spaces.

Researchers find how prostate tumors reshape the immune microenvironment

Researchers at The University of Texas MD Anderson Cancer Center have identified a previously unknown pathway that prostate tumors leverage to suppress the immune system by reshaping the tumor microenvironment to resist treatment. A combined targeted therapy approach improved antitumor responses and prolonged survival in preclinical models of castration-resistant prostate cancer.

The study, published in Cancer Discovery, was led by Di Zhao, Ph.D., associate professor of Experimental Radiation Oncology, and Nicholas Navin, Ph.D., chair of Systems Biology. The findings suggest that tumors use this pathway as a backup physical defense and may explain why therapies targeting the B7-H3 immune checkpoint may show limited efficacy when used alone.

Our study shows that the immune checkpoint B7-H3 does more than put the brakes on the immune system; it also reshapes the cells surrounding a tumor to help cancer grow and resist treatment. By uncovering how this process works, we identified a promising strategy that combines B7-H3-targeted therapy with MEK inhibition. These findings could help guide more effective, personalized treatments for patients whose tumors are unlikely to respond to B7-H3 therapy alone.

Tissue-specific aging clocks map structured aging-modulatory drug-score patterns across 49 human tissue and cell-line categories

Aging clocks are typically trained on pooled multi-tissue data, implicitly assuming that aging is uniform across organs. Here we challenge this assumption by constructing 49 transcriptomic clocks across 47 tissue types and 2 cell-line categories from GTEx v8 (948 unique donors contributing to the retained clock categories) using donor-grouped cross-validation. Clocks achieved a median Pearson r of 0.543 (best: artery aorta, r = 0.855). We identified 10,253 unique clock genes, of which 69.2% appeared in only one tissue; however, null simulation confirmed that this low overlap is the expected consequence of sparse elastic-net selection rather than biological tissue-specificity. We then projected 3,926 LINCS L1000 compound-name entries onto each clock to build a drug × category age-reversal matrix. At a permissive threshold (|score|> 1.0), 94.2% of drugs showed mixed score directions (positive in some categories and negative in others). This permissive mixed-direction proportion was descriptive and did not itself exceed shuffled expectations. Under a more stringent exploratory criterion requiring |score|> 2.0 in at least three categories in each direction, 2.8% of compound entries showed pronounced bidirectional divergence, compared with approximately 0.1% under the shuffled null. Compound rankings remained stable when analysis was restricted to the 34 clocks with r ≥ 0.5 (Spearman ρ = 0.897 versus the full analysis). A Jaccard-based enrichment statistic yielded highly concordant compound rankings (median per-category Spearman ρ = 0.961), indicating that results were not artifacts of the enrichment method. After Benjamini–Hochberg FDR correction across all 192,374 drug-category pairs, only 0.79% reached FDR < 0.05, indicating that individual drug-category calls require caution. Rapamycin showed net pro-aging transcriptional signatures in our system; we explicitly emphasize that transcriptome-based scores and organismal lifespan are distinct endpoints. DepMap CRISPR analysis was repeated after GTEx-based standardization across 13 matched categories, although individual gene-level results were limited. Four statistical robustness tests confirmed model stability. These findings describe category-associated drug-score patterns and provide a tissue-aware resource for generating hypotheses about drug responses, while cautioning against over-interpretation of individual drug-category predictions without experimental validation.

A Deep‐Learning Based Biomarker of Systemic Cellular Senescence Burden to Predict Mortality and Health Outcomes

Spearman correlation coefficients were calculated between the SASP Score and chronological age, biological aging measures—including telomere length, proteomic aging clock (PAC) (Kuo et al. 2024), healthspan proteomic score (HPS) (Kuo et al. 2025), biological age (BioAge) (Sayed et al. 2021), phenotypic age (PhenoAge) (Diniz et al. 2017)—as well as other baseline aging traits including a 49-item frailty index (Williams et al. 2019) in the UK Biobank test sample. Details on the development of these biological aging measures are provided in the cited references, and the field IDs used to extract the relevant data are listed in Table S1.

The distribution of the SASP Score was compared across subgroups based on a common disease risk factor, including age group, sex, frailty status, waist-to-hip ratio group, and smoking status. Group differences were assessed using Wilcoxon rank-sum tests, with p-values adjusted for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) method (Benjamini and Hochberg 1995). Additionally, the SASP Score distributions in subgroups were visualized using violin plots.

Cox proportional hazards models were used to investigate the associations of the standardized SASP Score with mortality and multiple aging-related diseases during follow-up (censored at death, or the last follow-up date of hospital inpatient data, whichever occurred first), with adjustments for baseline covariates. Mortality was ascertained through linkage to national death registries, and disease outcomes were derived using the UKB first occurrence data, which integrate multi-source data based on ICD-10 codes. Baseline covariates—including age, sex, ethnicity, education level, Townsend deprivation index (higher values associated with more material deprivation), smoking status (never, previous, current smoker), alcohol drinker status (never, previous, current drinker), systolic blood pressure, and BMI—were collected from UK Biobank online questionnaires or physical measurements at the baseline assessment. UK Biobank field IDs used to extract covariates and outcomes are provided in Table S1. Hazard ratios for each condition per SD increase in the SASP Score were reported with the multiple testing adjusted p-values using the FDR method. Model discrimination was evaluated using Harrell’s C-index, and the 95% confidence intervals were computed using a normal approximation based on the standard error of the C estimate.

RNA-Based Gene Editing Tool Combats Diseases With Multiple Mutations

Investigators from Mass General Brigham and Beth Israel Deaconess Medical Center have developed STITCHR, a new gene editing tool that can insert therapeutic genes into specific locations without causing unwanted mutations. The system can be formulated completely as RNA, dramatically simplifying delivery logistics compared to traditional systems that use both RNA and DNA. By inserting an entire gene, the tool offers a one-and-done approach that overcomes hurdles from CRISPR gene editing technology—which is programmed to correct individual mutations—offering a promising step forward for gene therapy. Results are published in Nature.

“CRISPR has revolutionized how we think about gene editing, but it has limitations. CRISPR can’t target every location in the genome, and it can’t fix the thousands of mutations present in diseases like cystic fibrosis,” said co-senior author Omar Abudayyeh, PhD, an investigator at the Gene and Cell Therapy Institute (GCTI) at Mass General Brigham and Engineering in Medicine Division in the Department of Medicine at Brigham and Women’s Hospital (BWH). “When we started our lab, one of the big things we wanted to figure out was how to insert large pieces of genes, or even entire genes, to replace faulty ones. This would allow us to target every mutation for a disease with a single gene editing construct.”

STITCHR harnesses the power of enzymes from genetic elements called retrotransposons, which are found in all eukaryotic cells, including animals, fungi, and plants. They are often called “jumping genes” for their tendency to move around and insert themselves into the genome. The researchers recognized how the copy-and-paste mechanism they use to move could be repurposed to edit genes at specific locations.

[4K] Watch the FIRST Orbital Starship Mission!!!

SpaceX is targeting Starship Flight 14 NET Monday, September 28, 2026 at 7:15 a.m. CT (12:15 UTC). This will be the FIRST orbital launch attempt of Starship with Ship 41 and Booster 21, carrying the first operational batch of 26 Starlink V3 satellites on a roughly 10-hour mission of about six orbits at around 275 km before a Pacific Ocean splashdown. Booster 21 will splash down in the Gulf after boostback, making this a major step from prior suborbital tests toward sustained orbit, payload ops, and eventual full reuse.

Want to know why Starship’s V3 Booster only has 3 grid fins instead of 4? Watch this!! — • The Genius Reason Why SpaceX Deleted A Gri…

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Or become a YouTube member for some bonus perks as well! — / @everydayastronaut.

The best place for all your space merch needs!
https://everydayastronaut.com/shop/

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Continue reading “[4K] Watch the FIRST Orbital Starship Mission!!!” | >

Massimo Pigliucci on Story, Virtue, Character and AI

This conversation went live on December 1, 2022. One day after ChatGPT launched.

Neither of us had any idea what was coming. Massimo Pigliucci and I were talking about something far older than any chatbot: why brilliant people go bad.

Massimo is one of the rare minds who holds doctorates in both biology and philosophy, and he actually lives the #Stoicism he writes about. His book, The Quest for Character, centers on Socrates and his most gifted student, Alcibiades. Charming, talented, handsome, rich, personally mentored by the wisest man in Athens. And yet.

That “and yet” is the whole problem of #leadership, then and now.

We got into questions I still haven’t fully settled. Can virtue be taught at all, or only shown? Is the Roman idea of “the way of the ancestors” really just an organizing story, and do we need a new one? Did Gandhi and King change people through what they said or through how they lived? And is the #transhumanism dream of engineering out human flaws a solution, or a way of avoiding the harder work of character?

Massimo’s answer to that last one surprised me.

Continue reading “Massimo Pigliucci on Story, Virtue, Character and AI” | >

The Next Frontier Of AI Isn’t Intelligence, It’s Trust

Building trust requires explainability, transparency, and human control over AI’s learning and actions, preventing unapproved behavioral shifts. Compilence Inc. presents a solution: human-written rules (ELIA), a gate (ARC) to control AI’s knowledge and actions, and an external record (Tissue) for auditable facts. This architecture ensures human oversight, consistent behavior, and verifiable changes, answering who approved an AI’s altered behavior. This capability, crucial for care robots and neurotechnology, enables truly trustworthy and governable AI systems.

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